def bandpass_denoising(a, name, save_flag=False):
b_time = time.time()
grid = GV.grid_displacement_to_center(a.shape, GV.fft_mid_co(a.shape))
rad = GV.grid_distance_to_center(grid)
rad = np.round(rad).astype(np.int)
# create a mask that only center frequencies components will be left
curve = np.zeros(rad.shape)
# TODO: change the curve value as desired
curve[int(rad.shape[0] / 8) * 3:int(rad.shape[0] / 8) * 5,
int(rad.shape[1] / 8) * 3:int(rad.shape[1] / 8) * 5,
int(rad.shape[2] / 8) * 3:int(rad.shape[2] / 8) * 5] = 1
#perform FFT and filter the data with the mask and then transform the filtered data back
vf = ifftn(ifftshift((fftshift(fftn(a)) * curve)))
vf = np.real(vf)
end_time = time.time()
print('Bandpass de-noise takes', end_time - b_time, 's')
if save_flag:
img = (vf[:, :, int(vf.shape[2] / 2)]).copy()
# TODO: Change the image and tomogram saving path
img_path = '/Users/apple/Desktop/Lab/Zach_Project/Denoising_Result/Bandpass/' + str(
name) + '_BP.png'
plt.imsave(img_path, img, cmap='gray')
mrc_path = '/Users/apple/Desktop/Lab/Zach_Project/Denoising_Result/Bandpass/' + str(
name) + '_BP.mrc'
io_file.put_mrc_data(vf, mrc_path)
return img
def sphere_mask(shape, center=None, radius=None, smooth_sigma=None):
shape = N.array(shape)
v = N.zeros(shape)
if center is None:
center = (
shape - 1
) / 2.0 # IMPORTANT: following python convension, in index starts from 0 to size-1 !!! So (siz-1)/2 is real symmetry center of the volume
center = N.array(center)
if radius is None: radius = N.min(shape / 2.0)
grid = gv.grid_displacement_to_center(shape, mid_co=center)
dist = gv.grid_distance_to_center(grid)
v[dist <= radius] = 1.0
if smooth_sigma is not None:
assert smooth_sigma > 0
v_s = N.exp(-((dist - radius) / smooth_sigma)**2)
v_s[v_s < N.exp(
-3
)] = 0.0 # use a cutoff of -3 looks nicer, although the tom toolbox uses -2
v[dist >= radius] = v_s[dist >= radius]
return v
def filter_given_curve(v, curve):
grid = GV.grid_displacement_to_center(v.shape, GV.fft_mid_co(v.shape))
rad = GV.grid_distance_to_center(grid)
rad = N.round(rad).astype(N.int)
b = N.zeros(rad.shape)
for (i, a) in enumerate(curve):
b[(rad == i)] = a
vf = ifftn(ifftshift((fftshift(fftn(v)) * b)))
vf = N.real(vf)
return vf
def ssnr__get_rad(siz):
grid = GV.grid_displacement_to_center(siz, GV.fft_mid_co(siz))
rad = GV.grid_distance_to_center(grid)
return rad